1. Field of Invention
The invention is related to a 3D (standing for three-dimensional) animation generation method used in digital multimedia, especially related to a 3D animation generation method using high-level motion scripts.
2. Related Art
In recent years, the application areas of computers have been broadened by their increasing computation power. With the advance of digital multimedia techniques, mass media also use computers to produce and deliver contents. In addition, recreation companies have already employed computer-based techniques to create animations and synthesize virtual characters in computer games. How to generate vivid and controllable character animations becomes an important issue in the areas of computer animation and video games.
In the traditional animation production, the motions of each character are drawn frame by frame by animators. Even for keyframes, describing a pose requires setting the angles of all joints, and hence requires setting about 20 to 60 parameters for each frame. As a result, it is difficult to animate and control virtual characters on the fly. Besides, the keyframe method heavily relies on animators' skills and experiences to produce vivid human animations. Another approach is known as the kinematics-based animation production method. When creating human animations, the method calculates the translation and rotation parameters of the end-effectors, the angles of joints, centers of gravity and roots by using techniques of biomechanics to generate vivid animations. Due to the high complexity of human motions, it is difficult to find good approximate motion equations. Hence, the application of this method is restricted, and is usually used in the syntheses of locomotion animations.
Dynamics is another method for simulating and generating motions by formulating the mass, inertia and angular moment of objects. However, simulating complicated joint systems such as human beings consumes a lot of computation power. Hence, it is difficult to generate animations by real-time dynamic simulation. The latest method employs 3D motion sensors to capture human motions. Since the captured motion data are guaranteed to fulfill the constraints in dynamics, the captured motion data are more vivid than those obtained by the prior methods. However, motion capture equipments are expensive and both capture and data editing processes are time-consuming. To reduce these costs, the reuse of the captured motion data becomes an important research issue. Recently, motion graphs and motion texture proposed novel control mechanisms to synthesize a new motion based on the existing motion data. However, these approaches still remain some difficulties such as long preprocessing time, and unexpected transitions. Moreover, the connection between high-level motion control and low-level mathematical models developed by these systems is unclear.